1. Field of the Invention
The present invention relates to information filtering and data mining. More specifically, the invention relates to technologies for extracting relevant information regarding the opinions of other users for presentation to a current user.
2. Description of the Related Art
Over the past several decades, advances in many aspects of computer technology, including enhanced capabilities for processing, storing, and networking digital data, have vastly increased the amounts and types of information available to the general public. For example, a repository of information for use by international camping enthusiasts includes descriptions of hundreds of thousands of campsites and hiking trails. A database of medical journal articles holds millions of articles. One popular online search engine is now able to access over 150 million visual images for display to viewers. Often, the problem for users of such systems is not how to access information on a desired topic, but how to sort through, how to make sense of, and how to become familiar with the huge amounts of data available. System administrators often strive to provide enjoyable ways for users to take best advantage of the data available to them, while minimizing the difficulty and frustration involved.
Similarly, with the increasing popularity of the Internet and the World Wide Web, it has also become common for online stores to set up websites and other types of interactive telecommunications systems for marketing and selling goods from online catalogs that can comprise millions of products. One problem facing online stores, and especially those with extensive product lines, is the challenge of encouraging users who are viewing a product catalog or other repository of information to stay at the site, to browse widely through the available products, and to make a purchase. Unlike proprietors of so-called “brick and mortar” establishments, administrators of online systems have very few techniques at their disposal for creating an inviting environment that encourages users to stay at the site, to interact with other users, and to look at the descriptions of a variety of products.
At the same time, users at such online stores face the challenge of making informed purchases in spite of the fact that they cannot physically inspect the products via the website and typically cannot talk to a salesperson. One way that online stores seek to assist users with this challenge is by presenting product reviews, written either by professional reviewers or by other users. Typically, two shortcomings exist with such product reviews from the point of view of the users: (1) the user has little basis upon which to judge the relevance of the reviewer's opinion other than the content of the review itself, and (2) reviews are generally displayed for products that the user has already selected for display, and thus do not lead the user to other products of potential interest.
An online merchant or other system administrator can also seek to assist users by recommending items that the merchant believes will be of interest to the user. Such recommendations typically suggest items that have been found to be statistically similar to the merchant's conception or profile of the user's interests. One shortcoming of current recommendation systems is that reliable recommendations generally cannot be generated for a user until an accurate profile has been developed of the user's interest. Systems with these requirements are typically not flexible enough to provide recommendations for users with no such profile or to accommodate a user's short-term needs, such as when investigating a new research topic or gift shopping for someone very different than themselves.
Another shortcoming of recommendation systems is that, as is the case with product reviews, users have no way to judge the relevance of the recommendation to themselves. Statistical recommendations, whether based on a similarity metric of content analysis or the purchase histories of other users or some other metric, cannot provide the compelling force of a recommendation from another human being with known similar or interesting preferences.
In addition to the problem of making informed purchases online, users often find online shopping environments to be impersonal and unfriendly and to lack opportunities for human interaction, for participation, and for self-expression. Similar problems face the users of other types of large repositories of information. Repository administrators who wish to provide these experiences in order to encourage users to make frequent and satisfying use of their systems generally have few techniques at their disposal by which to do so.